MLEM now offers deployment to Kubernetes and Sagemaker with a single command.
Serve a model by exposing its methods as endpoints.
def serve(
model: Union[str, MlemModel],
server: Union[Server, str],
**server_kwargs,
)from mlem.api import serve
serve(model, "fastapi")This API is the underlying mechanism for the mlem serve command and allows us to locally serve a model by exposing its methods as endpoints. This makes it possible to easily make requests (for inference or otherwise) against the served model.
model (required) - The model to serve.server (required) - Out-of-the-box supported one is "fastapi".server_kwargs (required) - Additional kwargs to pass to the server.None
None
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from mlem.core.objects import MlemModel
from mlem.contrib.fastapi import FastAPIServer
from mlem.api import serve
train, target = load_iris(return_X_y=True)
model = DecisionTreeClassifier().fit(train, target)
m = MlemModel.from_obj(model, sample_data=train)
server_obj = FastAPIServer(port=9000)
serve(m, server_obj)